2023 28th International Computer Conference, Computer Society of Iran (CSICC)最新文献

Distinguishing between Tip-Toe and Normal Gaits using Knee Angle Signal from the Skeleton Data Gathered by using OpenPose Module 利用OpenPose模块采集的骨骼数据中膝关节角度信号进行足尖和正常步态的区分

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105316

Shahzad Moghimifar, F. Farokhi

{"title":"Distinguishing between Tip-Toe and Normal Gaits using Knee Angle Signal from the Skeleton Data Gathered by using OpenPose Module","authors":"Shahzad Moghimifar, F. Farokhi","doi":"10.1109/CSICC58665.2023.10105316","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105316","url":null,"abstract":"Background: This article offers a novel method for human gait recognition with OpenPose Module. The suggested technique emphasizes the detection of specific uncommon gait like tiptoe. Methods: For this purpose, an OpenPose module was employed to extract skeleton and joints, and the gaits in straight walking were obtained by using knee signal. Additionally, the similarity between the detected and normal (as reference) gaits was assessed with a dynamic time warping algorithm. Resultss: The algorithm outputs, normalizing factor, and unnormalized distance between input and reference signals were utilized to classify the normal and tiptoe gaits. Two features were selected as the most important features for classification. The proposed approach was tested among 70 individuals, which reached an accuracy of 92% between tiptoe and normal walking. Conclusions: The strong correlations with reference measurements support the recommended method for estimating tiptoe gait. The knee angle analysis during gait can be considered an identifier in other walking disorders. The mentioned difference may be a particular identifier for each walking disease. Accordingly, the present study results reflected the potential of the suggested approach to be used in identifying other walking disorders. Besides, walking evaluation can be done by getting similar to a typical gait.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117347392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Convolutional-Transformer Neural Network Architecture for Diagnosis of Pneumothorax 一种新的用于气胸诊断的卷积-变换神经网络结构

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105407

Amir Sanati, Mansoureh A. Dashtestani, H. Rostami, Saeed Talatian Azad

引用次数: 0

A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection 一种以数据为中心的方法，通过分布外检测来改进对抗训练

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105351

Mohammad Azizmalayeri, Arman Zarei, Alireza Isavand, M. T. Manzuri, M. Rohban

{"title":"A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection","authors":"Mohammad Azizmalayeri, Arman Zarei, Alireza Isavand, M. T. Manzuri, M. Rohban","doi":"10.1109/CSICC58665.2023.10105351","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105351","url":null,"abstract":"Current machine learning models achieve super-human performance in many real-world applications. Still, they are susceptible against imperceptible adversarial perturbations. The most effective solution for this problem is adversarial training that trains the model with adversarially perturbed samples instead of original ones. Various methods have been developed over recent years to improve adversarial training such as data augmentation or modifying training attacks. In this work, we examine the same problem from a new data-centric perspective. For this purpose, we first demonstrate that the existing model-based methods can be equivalent to applying smaller perturbation or optimization weights to the hard training examples. By using this finding, we propose detecting and removing these hard samples directly from the training procedure rather than applying complicated algorithms to mitigate their effects. For detection, we use maximum softmax probability as an effective method in out-of-distribution detection since we can consider the hard samples as the out-of-distribution samples for the whole data distribution. Our results on SVHN and CIFAR-10 datasets show the effectiveness of this method in improving the adversarial training without adding too much computational cost.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122148476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Fuzzy Integral Approach for Ensembling Unsupervised Feature Selection Algorithms 集成无监督特征选择算法的模糊积分方法

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105330

Amin Hashemi, M. B. Dowlatshahi

引用次数: 0

GPS: A graph-based approach to portfolio selection GPS:基于图表的投资组合选择方法

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105331

Fatemeh Rezaee, Jalal Ahmadi, Saman Haratizadeh

引用次数: 0

A Comprehensive Architecture Platform for Smarter Universities 智慧大学的综合架构平台

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105323

Amir Saberi Varzaneh, Behnaz Merikhi, Marzieh Saberi Varzaneh, Mohammad Hossein Alam Varzaneh Isfahani, H. Kalahroodi, Mohammad Fatemipour Sharabiany

{"title":"A Comprehensive Architecture Platform for Smarter Universities","authors":"Amir Saberi Varzaneh, Behnaz Merikhi, Marzieh Saberi Varzaneh, Mohammad Hossein Alam Varzaneh Isfahani, H. Kalahroodi, Mohammad Fatemipour Sharabiany","doi":"10.1109/CSICC58665.2023.10105323","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105323","url":null,"abstract":"One of the most crucial infrastructure requirements of integrated systems is designing and providing a comprehensive architecture for implementation, support and improvement of their smart services or platforms. And without exception, smart University would also require a responsive architecture for the integration and consolidation of its current and future systems and services. Providing higher level of academic services, integration and consolidation support, and cost accounting (including “cloud storage”, “computing”, “network communication and bandwidth” and energy consumption) can be placed as pivotal issues among smart university services. In this article, a basic definition of a smart university and its integrated services will be presented at first. Then, strengths, weaknesses and covering range of conventional smart systems architectures are examined and compared. In the following, a novel perspective of smarter universities and regarding consolidated services are introduced and a comprehensive architecture for the establishment of smart university services and systems is presented. afterward, layers of this architecture, their efficiency and connections are examined. Finally, the covering and responsiveness of the comprehensive architecture are examined from different perspectives; And in an actual example, its optimization is compared to a previously proposed architecture that was implemented in Shahid Beheshti University of Tehran-Iran. In addition, as well, the other results are thoroughly discussed.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114899447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fashion Compatibility Learning Via Triplet-Swin Transformer 时尚兼容性学习通过三胞胎-游泳变压器

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105392

Hosna Darvishi, R. Azmi, Fatemeh Moradian, Maral Zarvani

{"title":"Fashion Compatibility Learning Via Triplet-Swin Transformer","authors":"Hosna Darvishi, R. Azmi, Fatemeh Moradian, Maral Zarvani","doi":"10.1109/CSICC58665.2023.10105392","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105392","url":null,"abstract":"Owing to the rising standard of living, personal appearance, mainly matching clothes, is essential to people. Because the right clothes not only can beautify their appearance directly but can also increase their self-confidence. This study aims to help users find a matching pair of clothes by considering the intricate details to choose suitable and compatible clothes. In this paper, increasing the efficiency of feature extraction is very important because fashion has a complicated concept, and the extraction of features such as the overall shape, design, and texture of clothes can significantly impact understanding and learning the compatibility of clothes. Therefore, suitable global features can help a lot in understanding the compatibility of clothes. Transformers can extract global features better than convolution networks. We use Swin Transformer networks to extract the image features. We have trained a Triplet-Swin network to learn fashion compatibility, which achieves better accuracy than previous methods. We evaluated our model with AUC and FITB metrics and the Polyvore Outfit dataset.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114913180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Virtual Machine Placement Strategy Using Clustering and Genetic Algorithm for increasing cloud performance and power saving 基于聚类和遗传算法的虚拟机布局策略以提高云性能和节能

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105329

Alireza Sajadinia, Alireza Yari

{"title":"Virtual Machine Placement Strategy Using Clustering and Genetic Algorithm for increasing cloud performance and power saving","authors":"Alireza Sajadinia, Alireza Yari","doi":"10.1109/CSICC58665.2023.10105329","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105329","url":null,"abstract":"Cloud computing is one of the most critical technologies of the century. However, it faces many challenges in both the adoption and operation phases. In the bigger picture, resolutions leading to increasing performance and decreasing costs are highly valued. Cloud computing has two types of expenses, Initial costs and routine expenditures such as human resources, maintenance fees, and electricity bills which form a significant part of the total costs of cloud infrastructure. Improving efficiency using optimal resource usage and decreasing energy waste would be possible through correct resource allocation. Intelligent algorithms can achieve maximum efficiency by placing virtual machines in the minimum number of physical servers and using the resources of these servers to the best. The main goal of this research is to reduce electricity consumption and the cooling required for under-utilized servers while preventing network congestions. The proposed algorithm of this research uses a multi-objective genetic algorithm and clusters the virtual machines to reduce the genetic algorithm execution time. The evaluation of the proposed algorithm implementation indicates that in this method convergence time is faster than the algorithms that lack clustering. As the secondary objective, the proposed algorithm distributes network traffic between physical servers to reduce network bottlenecks.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125073020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Question Summarization Method based-on Deep Learning in Persian Language 基于深度学习的波斯语问题摘要方法

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105324

A. Ghafouri, Iman Barati, Mohammad Hossein Elahimanesh, Hamidreza Hasanpour

引用次数: 0

Real-time Steal Recognition on CCTV-Based Videos for Embedded Systems 嵌入式系统中基于cctv视频的实时盗窃识别

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105393

Sepehr Kerachi, Arian Komaei Koma, Hadi Asharioun

引用次数: 0